Abstract: With the development of social network,new words appear ceaselessly.The appearance of new word tends to characterize the social hot spot or represent certain public mood.The new word detection and emotional tendency judgment provide a new way for the public mood forecast.We constructed the deep conditional random fields model for the sequence labeling,introduced part of speech,character position,the ability of word formation as features,and combined it with the crowd sourcing network dictionary and the other third party dictionary.Traditional method based on emotional dictionary is difficult to judge the new word emotional tendency.We expressed word as a vector of K dimension based on neural network language model in order to find the nearest words to the new word in the vector space.According to the emotional tendency of these words and the distance between them and the new word,the new word sentiment is judged.The experiment on corpus of Peking university demonstrates the feasibility of the proposed model and method,in which the new word detection F-value is 0.991,and the emotion recognition accuracy is 70%.

Key words: New word detection,Conditional random fields,Deep structured model,Emotional tendency judgment,Neural network language model